MEMPSODE: A global optimization software based on hybridization of population-based algorithms and local searches
| Main Author: | CPC, Mendeley |
|---|---|
| Other Authors: | Voglis, C., Parsopoulos, K.E., Papageorgiou, D.G., Lagaris, I.E., Vrahatis, M.N. |
| Format: | Dataset |
| Terbitan: |
Mendeley
, 2012
|
| Subjects: | |
| Online Access: |
https:/data.mendeley.com/datasets/s4thf738k7 |
| ctrlnum |
0.17632-s4thf738k7.1 |
|---|---|
| fullrecord |
<?xml version="1.0"?>
<dc><creator>CPC, Mendeley</creator><title>MEMPSODE: A global optimization software based on hybridization of population-based algorithms and local searches </title><publisher>Mendeley</publisher><description>Abstract
We present MEMPSODE, a global optimization software tool that integrates two prominent population-based stochastic algorithms, namely Particle Swarm Optimization and Differential Evolution, with well established efficient local search procedures made available via the Merlin optimization environment. The resulting hybrid algorithms, also referred to as Memetic Algorithms, combine the space exploration advantage of their global part with the efficiency asset of the local search, and as expecte...
Title of program: MEMPSODE (MEMetic Particle Swarm Optimization and Differential Evolution)
Catalogue Id: AELM_v1_0
Nature of problem
Optimization is a valuable mathematical tool for solving a plethora of scientific and engineering problems. Usually, the underlying problems are modeled with objective functions whose minimizers (or maximizers) correspond to the desired solutions of the original problem. In many cases, there is a multitude of such minimizers that correspond to solutions either locally, i.e., in their close neighborhood, or globally, i.e., with respect to the whole search space. There is a significant number of e ...
Versions of this program held in the CPC repository in Mendeley Data
AELM_v1_0; MEMPSODE (MEMetic Particle Swarm Optimization and Differential Evolution); 10.1016/j.cpc.2012.01.010
This program has been imported from the CPC Program Library held at Queen's University Belfast (1969-2018)</description><subject>Computational Physics</subject><subject>Computational Method</subject><contributor>Voglis, C.</contributor><contributor>Parsopoulos, K.E.</contributor><contributor>Papageorgiou, D.G.</contributor><contributor>Lagaris, I.E.</contributor><contributor>Vrahatis, M.N.</contributor><type>Other:Dataset</type><identifier>10.17632/s4thf738k7.1</identifier><rights>Computer Physics Communications Journal Licence</rights><rights>https://www.elsevier.com/about/policies/open-access-licenses/elsevier-user-license/cpc-license/</rights><relation>https:/data.mendeley.com/datasets/s4thf738k7</relation><date>2012-05-01T11:00:00Z</date><recordID>0.17632-s4thf738k7.1</recordID></dc>
|
| format |
Other:Dataset Other |
| author |
CPC, Mendeley |
| author2 |
Voglis, C. Parsopoulos, K.E. Papageorgiou, D.G. Lagaris, I.E. Vrahatis, M.N. |
| title |
MEMPSODE: A global optimization software based on hybridization of population-based algorithms and local searches |
| publisher |
Mendeley |
| publishDate |
2012 |
| topic |
Computational Physics Computational Method |
| url |
https:/data.mendeley.com/datasets/s4thf738k7 |
| contents |
Abstract
We present MEMPSODE, a global optimization software tool that integrates two prominent population-based stochastic algorithms, namely Particle Swarm Optimization and Differential Evolution, with well established efficient local search procedures made available via the Merlin optimization environment. The resulting hybrid algorithms, also referred to as Memetic Algorithms, combine the space exploration advantage of their global part with the efficiency asset of the local search, and as expecte...
Title of program: MEMPSODE (MEMetic Particle Swarm Optimization and Differential Evolution)
Catalogue Id: AELM_v1_0
Nature of problem
Optimization is a valuable mathematical tool for solving a plethora of scientific and engineering problems. Usually, the underlying problems are modeled with objective functions whose minimizers (or maximizers) correspond to the desired solutions of the original problem. In many cases, there is a multitude of such minimizers that correspond to solutions either locally, i.e., in their close neighborhood, or globally, i.e., with respect to the whole search space. There is a significant number of e ...
Versions of this program held in the CPC repository in Mendeley Data
AELM_v1_0; MEMPSODE (MEMetic Particle Swarm Optimization and Differential Evolution); 10.1016/j.cpc.2012.01.010
This program has been imported from the CPC Program Library held at Queen's University Belfast (1969-2018) |
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IOS7969.0.17632-s4thf738k7.1 |
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Universitas Islam Indragiri |
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onesearch.perpusnas.go.id |
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804 |
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library:university library |
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Teknologi Pangan UNISI |
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2816 |
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Artikel mulono |
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7969 |
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INDRAGIRI HILIR |
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RIAU |
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1 |
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IOS7969 |
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2020-04-08T08:28:09Z |
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2020-04-08T08:28:09Z |
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dc |
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1686587743966068736 |
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17.538404 |